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1 | | -# Basic Statistics With Python []() |
2 | | -[Last updated on 19th Aug 2022] |
3 | | - |
4 | | -This is a series of lecture notes that I prepared in my statistics tutorial (used prepared for one-night new-hire statistical training in hedge fund), which are supposed to be concise and straightforward, without any unnecessary proofs or derivation. The tutorials will cover most of the core statistic concepts starting from descriptive statistics to statistic inferences and hypothesis testing, some probability distribution will also be refreshed. |
5 | | - |
6 | | -Once you have walked through the tutorial notes, you should be confident to move further to notes of <a href='https://github.com/MacroAnalyst/Basic_Econometrics_With_Python' target='_blank'>Econometrics</a>, Bayesian statistics/econometrics, which I will upload in the near furture. |
7 | | - |
8 | | -## Who Can Benefit From These Notes |
9 | | -Perfect for university students who wants to have a walkthrough of core structure of **frequentist statistics**, also very beneficial for practioners, such as junior quantitative analysts, who wants to refresh their knowledge as fast as possible (i.e. within 3 days). All the examples in the notes are demonstrated by Python, including all figures and charts. |
10 | | - |
11 | | -## Prerequisites |
12 | | -Though the lectures are introductory level, it would be ideal that attendants have a slight exposure to probability theory. |
13 | | - |
14 | | -And you would benefit more from the tutorials if you have basic knowledge of: |
15 | | -- [x] NumPy |
16 | | -- [x] Matplotlib |
17 | | -- [x] Pandas |
18 | | - |
19 | | -## Contents |
20 | | -It is advisable to either open the notebooks in Jupyter nbviewers (links below) or download them, since github has frequent rendering glitches in LaTeX and sometimes even missing a plot. |
21 | | - |
22 | | -[Lecture 1 - Descriptive Statistics](https://nbviewer.jupyter.org/github/MacroAnalyst/Basic_Statistics_With_Python/blob/main/Chapter%201%20-%20Descriptive%20Statistics.ipynb)<br> |
23 | | -[Lecture 2 - Probability Review](https://nbviewer.jupyter.org/github/MacroAnalyst/Basic_Statistics_With_Python/blob/main/Chapter%202%20-%20Probability%20Review.ipynb)<br> |
24 | | -[Lecture 3 - Point and Interval Estimation](https://nbviewer.jupyter.org/github/MacroAnalyst/Basic_Statistics_With_Python/blob/main/Chapter%203%20-%20Point%20and%20Interval%20Estimation.ipynb)<br> |
25 | | -[Lecture 4 - Hypothesis Testing](https://nbviewer.jupyter.org/github/MacroAnalyst/Basic_Statistics_With_Python/blob/main/Chapter%204%20-%20Hypothesis%20Testing.ipynb)<br> |
26 | | -[Lecture 5 - Analysis of Variance and Chi-Squared Test](https://nbviewer.jupyter.org/github/MacroAnalyst/Basic_Statistics_With_Python/blob/main/Chapter%205%20-%20Analysis%20of%20Variance%20and%20Chi-Squared%20Test.ipynb)<br> |
27 | | - |
28 | | -## Screen Capture Examples |
29 | | - |
30 | | - |
31 | | - |
32 | | - |
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35 | | - |
| 1 | +# Basic Statistics With Python []() |
| 2 | +[Last updated on 19th Aug 2022] |
| 3 | + |
| 4 | +This is a series of lecture notes that I prepared in my statistics tutorial (used prepared for one-night new-hire statistical training in hedge fund), which are supposed to be concise and straightforward, without any unnecessary proofs or derivation. The tutorials will cover most of the core statistic concepts starting from descriptive statistics to statistic inferences and hypothesis testing, some probability distribution will also be refreshed. |
| 5 | + |
| 6 | +Once you have walked through the tutorial notes, you should be confident to move further to notes of <a href='https://github.com/MacroAnalyst/Basic_Econometrics_With_Python' target='_blank'>Econometrics</a>, Bayesian statistics/econometrics, which I will upload in the near furture. |
| 7 | + |
| 8 | +## Who Can Benefit From These Notes |
| 9 | +Perfect for university students who wants to have a walkthrough of core structure of **frequentist statistics**, also very beneficial for practioners, such as junior quantitative analysts, who wants to refresh their knowledge as fast as possible (i.e. within 3 days). All the examples in the notes are demonstrated by Python, including all figures and charts. |
| 10 | + |
| 11 | +## Prerequisites |
| 12 | +Though the lectures are introductory level, it would be ideal that attendants have a slight exposure to probability theory. |
| 13 | + |
| 14 | +And you would benefit more from the tutorials if you have basic knowledge of: |
| 15 | +- [x] NumPy |
| 16 | +- [x] Matplotlib |
| 17 | +- [x] Pandas |
| 18 | + |
| 19 | +## Contents |
| 20 | +It is advisable to either open the notebooks in Jupyter nbviewers (links below) or download them, since github has frequent rendering glitches in LaTeX and sometimes even missing a plot. |
| 21 | + |
| 22 | +[Lecture 1 - Descriptive Statistics](https://nbviewer.jupyter.org/github/MacroAnalyst/Basic_Statistics_With_Python/blob/main/Chapter%201%20-%20Descriptive%20Statistics.ipynb)<br> |
| 23 | +[Lecture 2 - Probability Review](https://nbviewer.jupyter.org/github/MacroAnalyst/Basic_Statistics_With_Python/blob/main/Chapter%202%20-%20Probability%20Review.ipynb)<br> |
| 24 | +[Lecture 3 - Point and Interval Estimation](https://nbviewer.jupyter.org/github/MacroAnalyst/Basic_Statistics_With_Python/blob/main/Chapter%203%20-%20Point%20and%20Interval%20Estimation.ipynb)<br> |
| 25 | +[Lecture 4 - Hypothesis Testing](https://nbviewer.jupyter.org/github/MacroAnalyst/Basic_Statistics_With_Python/blob/main/Chapter%204%20-%20Hypothesis%20Testing.ipynb)<br> |
| 26 | +[Lecture 5 - Analysis of Variance and Chi-Squared Test](https://nbviewer.jupyter.org/github/MacroAnalyst/Basic_Statistics_With_Python/blob/main/Chapter%205%20-%20Analysis%20of%20Variance%20and%20Chi-Squared%20Test.ipynb)<br> |
| 27 | + |
| 28 | +## Screen Capture Examples |
| 29 | + |
| 30 | + |
| 31 | + |
| 32 | + |
| 33 | + |
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| 35 | + |
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